Efficient parallel processing of competitive learning algorithms

Kentaro Sano, Shintaro Momose, Hiroyuki Takizawa, Hiroaki Kobayashi, Tadao Nakamura

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Vector quantization (VQ) is an attractive technique for lossy data compression, which has been a key technology for data storage and/or transfer. So far, various competitive learning (CL) algorithms have been proposed to design optimal codebooks presenting quantization with minimized errors. Although algorithmic improvements of these CL algorithms have achieved faster codebook design than conventional ones, limitations of speedup still exist when large data sets are processed on a single processor. Considering a variety of CL algorithms, parallel processing on flexible computing environment, like general-purpose parallel computers is in demand for a large-scale codebook design. This paper presents a formulation for efficiently parallelizing CL algorithms, suitable for distributee-memory parallel computers with a message-passing mechanism. Based on this formulation, we parallelize three CL algorithms: the Kohonen learning algorithm, the MMPDCL algorithm and the LOJ algorithm. Experimental results indicate a high scalability of the parallel algorithms on three different types of commercially available parallel computers: IBM SP2, NEC AzusA and PC duster.

Original languageEnglish
Pages (from-to)1361-1383
Number of pages23
JournalParallel Computing
Issue number12
Publication statusPublished - 2004 Dec


  • Competitive learning
  • Law-of-the-jungle algorithm
  • MMPDCL algorithm
  • Optimal codebook design
  • Parallel processing
  • Vector quantization


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